FRAMEWORK FOR IMPROVED NATURAL LANGUAGE UNDERSTANDING AND INTERACTION

Abstract

ABSTRACT “FRAMEWORK FOR IMPROVED NATURAL LANGUAGE UNDERSTANDING AND INTERACTION” The present invention provides framework for improved natural language understanding and interaction that incorporates advanced techniques such as hierarchical memory networks, dynamic context windows, and reinforcement learning to efficiently manage conversational context, optimize response strategies, and handle ambiguous inputs. The framework includes an input layer for tokenizing text or speech, an NLP model (e.g., BERT or GPT-based) for interpreting user inputs, and a memory management layer to store and retrieve relevant context. The reinforcement learning layer dynamically adapts the system’s behavior based on user feedback, improving dialogue coherence. Additionally, a human-in-the-loop feedback mechanism enables continuous learning and refinement of the model. This system enhances the performance of conversational agents, making digital communication in Hausa more accessible and contextually aware. Figure 1

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to the field of Dialogue Systems and Conversational Agents, and more particularly, the present invention relates to the framework for improved natural language understanding and interaction.
BACKGROUND ART
[0002] The following discussion of the background of the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known, or part of the common general knowledge in any jurisdiction as of the application's priority date. The details provided herein the background if belongs to any publication is taken only as a reference for describing the problems, in general terminologies or principles or both of science and technology in the associated prior art.
[0003] Despite the widespread use of conversational agents and dialogue systems, there is limited research addressing the unique linguistic and cultural aspects of the Hausa language within these technologies. This research aims to identify and solve specific challenges in developing robust natural language understanding (NLU) for Hausa, focusing on maintaining coherent, context-aware conversation; managing ambiguity and clarification; handling dialectal diversity; and addressing lexical, syntactic, and cultural nuances. Through an in-depth analysis of Hausa's linguistic structure and user interaction patterns, this study will develop a framework that enhances intent recognition, response generation, and overall comprehension within Hausa-based dialogue systems.
[0004] The existing solution for a Hausa language chatbot primarily focuses on simple, interactive conversations, with basic capabilities for learning and storytelling. It employs PHP, MySQL, HTML, and JavaScript, making it accessible as a web-based application. However, current solutions lack advanced features like context management, ambiguity resolution, and dialect handling, limiting their depth and accuracy in Hausa.
[0005] In light of the foregoing, there is a need for Framework for improved natural language understanding and interaction that overcomes problems prevalent in the prior art associated with the traditionally available method or system, of the above-mentioned inventions that can be used with the presented disclosed technique with or without modification.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.
OBJECTS OF THE INVENTION
[0007] The principal object of the present invention is to overcome the disadvantages of the prior art by providing framework for improved natural language understanding and interaction.
[0008] Another object of the present invention is to provide framework for improved natural language understanding and interaction that warrants protection include advanced context management, reinforcement learning, human-in-the-loop feedback, and the application of contextualized understanding models specifically adapted for Hausa.
[0009] Another object of the present invention is to provide framework for improved natural language understanding and interaction that uses hierarchical memory networks and dynamic context windows to retain and retrieve conversational context over time, ensuring coherent, contextually aware dialogues. Reinforcement learning allows the system to adapt based on user interactions, dynamically refining responses and managing long-term data, which most traditional systems lack.
[0010] Another object of the present invention is to provide framework for improved natural language understanding and interaction that enables the chatbot to continually improve its response accuracy by learning from user feedback.
[0011] Another object of the present invention is to provide framework for improved natural language understanding and interaction that enhances intent recognition, addressing Hausa's unique syntactic and cultural nuances. Together, these features create a technically advanced and culturally relevant solution, distinguishing it from existing rule-based and static chatbot models.
[0012] The foregoing and other objects of the present invention will become readily apparent upon further review of the following detailed description of the embodiments as illustrated in the accompanying drawings.
SUMMARY OF THE INVENTION
[0013] The present invention relates to framework for improved natural language understanding and interaction. To address abovementioned challenges, the framework will incorporate advanced techniques, including hierarchical memory networks or dynamic context windows, to store and retrieve relevant conversational context efficiently. Additionally, reinforcement learning will be implemented to enable systems to manage and recall long-term conversational data dynamically. This approach will be supported by contextualized understanding models, such as BERT-based or GPT-based architectures, combined with reinforcement learning, to determine optimal strategies for asking clarification questions and managing ambiguous inputs. Human-in-the-loop systems will further allow agents to learn from user feedback, refining their interpretation of complex or ambiguous statements.
[0014] Functionality of the Layers:
- Input Layer: Tokenizes incoming text/speech.
- NLP Model: Interprets text/speech for intent and entities by fine-turning supervised learning. e.g BERT/GPT-based models to enhance language understanding.
- Memory Management Layer: Manages context using hierarchical networks to adjust based on conversation flow.
- RL Layer: Optimizes memory retrieval and updates by policy training (e.g Deep Q-Learning, Policy Gradient) to measure user engagement metrics by state and reward definition.
- Feedback Mechanism Layer: Processes user feedback for model learning using human-in-the loop system which enables continuous learning from user feedback, refining the model's interpretation of complex statements.
- Deployment: Deploying the model within a scalable infrastructure.
- Output Layer: Generates context-aware responses.
[0015] This research, in contrast, aims to enhance dialogue coherence, manage ambiguity, and recognize dialectal diversity through advanced methods like hierarchical memory networks and reinforcement learning. By incorporating contextualized understanding models (e.g., BERT or GPT) and human-in-the-loop feedback, this research approach will enable sophisticated, contextually-aware interactions and dynamic learning from user feedback, advancing Hausa language dialogue and conversation agents capabilities to a more adaptive and culturally attuned level.
[0016] Moreover, integration of these methodologies will improve the performance of conversational agents in Hausa, making digital communication more accessible and intuitive for Hausa-speaking users. This research will contribute significantly to the inclusion of underrepresented African languages in AI, laying the groundwork for future studies on linguistic diversity in conversational AI systems.
[0017] While the invention has been described and shown with reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0018] So that the manner in which the above-recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
[0019] These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:
[0020] Figure 1. Flow Chart of the Proposed Idea, in accordance with an exemplary embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0021] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[0022] As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0023] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[0024] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[0025] The present invention relates to framework for improved natural language understanding and interaction. To address abovementioned challenges, the framework will incorporate advanced techniques, including hierarchical memory networks or dynamic context windows, to store and retrieve relevant conversational context efficiently. Additionally, reinforcement learning will be implemented to enable systems to manage and recall long-term conversational data dynamically. This approach will be supported by contextualized understanding models, such as BERT-based or GPT-based architectures, combined with reinforcement learning, to determine optimal strategies for asking clarification questions and managing ambiguous inputs. Human-in-the-loop systems will further allow agents to learn from user feedback, refining their interpretation of complex or ambiguous statements.
[0026] Functionality of the Layers:
- Input Layer: Tokenizes incoming text/speech.
- NLP Model: Interprets text/speech for intent and entities by fine-turning supervised learning. e.g BERT/GPT-based models to enhance language understanding.
- Memory Management Layer: Manages context using hierarchical networks to adjust based on conversation flow.
- RL Layer: Optimizes memory retrieval and updates by policy training (e.g Deep Q-Learning, Policy Gradient) to measure user engagement metrics by state and reward definition.
- Feedback Mechanism Layer: Processes user feedback for model learning using human-in-the loop system which enables continuous learning from user feedback, refining the model's interpretation of complex statements.
- Deployment: Deploying the model within a scalable infrastructure.
- Output Layer: Generates context-aware responses.
[0027] This research, in contrast, aims to enhance dialogue coherence, manage ambiguity, and recognize dialectal diversity through advanced methods like hierarchical memory networks and reinforcement learning. By incorporating contextualized understanding models (e.g., BERT or GPT) and human-in-the-loop feedback, this research approach will enable sophisticated, contextually-aware interactions and dynamic learning from user feedback, advancing Hausa language dialogue and conversation agents capabilities to a more adaptive and culturally attuned level.
[0028] Moreover, integration of these methodologies will improve the performance of conversational agents in Hausa, making digital communication more accessible and intuitive for Hausa-speaking users. This research will contribute significantly to the inclusion of underrepresented African languages in AI, laying the groundwork for future studies on linguistic diversity in conversational AI systems.
[0029] This solution significantly advances dialogue and conversation agents in Hausa by incorporating advanced memory and contextual understanding systems tailored to this specific linguistic environment. Unlike conventional chatbots that focus on widely used languages, this agent utilizes hierarchical memory networks and dynamic context windows to handle nuanced, context-aware interactions in Hausa, retaining critical information over extended conversations. Reinforcement learning enables it to adapt dynamically, optimizing responses based on user feedback and even handling ambiguous statements effectively. Additionally, a human-in-the-loop feedback mechanism allows the chat bot to continuously improve by learning from user interactions, enhancing both comprehension and response accuracy over time.
[0030] Applications of the invention:
 Customer Support
 Language Learning
 Cultural Preservation
 Healthcare Communication
 E-Government Services
 Entertainment
 Research and Data Collection
 Social Interaction
 Accessibility Tools
[0031] Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the 5 embodiments shown along with the accompanying drawings but is to be providing the broadest scope consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and appended claims.
, Claims:CLAIMS
We Claim:
1) A framework for improved natural language understanding and interaction, the framework comprising:
- An input layer for tokenizing incoming text or speech;
- A natural language processing (NLP) model, based on BERT or GPT architectures, that interprets the tokenized text or speech to identify intent and entities through fine-tuned supervised learning;
- A memory management layer that uses hierarchical memory networks or dynamic context windows to store and retrieve relevant conversational context based on conversation flow;
- A reinforcement learning (RL) layer that optimizes memory retrieval and updates based on policy training, such as Deep Q-Learning or Policy Gradient, by defining states, rewards, and user engagement metrics;
- A feedback mechanism layer that processes user feedback using a human-in-the-loop system, enabling continuous model learning and refinement based on user interactions.
2) The framework as claimed in claim 1, wherein the RL layer dynamically adapts the conversation system's behavior based on the continuous feedback from users, enabling the system to handle ambiguous statements and adjust responses over time for enhanced dialogue coherence.
3) The framework as claimed in claim 1, wherein the memory management layer adjusts the context window size dynamically, enabling the system to manage long-term and short-term conversational context efficiently, thereby enhancing conversation flow and reducing context loss.
4) A method for enhancing dialogue coherence in a conversational agent, the method comprising:
- Tokenizing incoming text or speech from a user;
- Interpreting the tokenized input using an NLP model fine-tuned on supervised learning techniques;
- Storing and retrieving context using hierarchical memory networks or dynamic context windows to ensure relevant context is applied to ongoing conversation;
- Optimizing response strategies using reinforcement learning techniques based on user engagement and feedback;
- Continuously refining the conversational agent's performance using a human-in-the-loop feedback mechanism to handle complex and ambiguous statements more effectively.

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